Automatic Defect Classification (ADC) techniques are widely used in inspection and measurement of defects on patterned wafers in the semiconductor industry. ADC techniques detect the existence of defects, as well as automatically classify the defects by type in order to provide more detailed feedback on the production process and to reduce the load on human inspectors. ADC techniques are used, for example, to distinguish among types of defects arising from particulate contaminants on a wafer surface and defects associated with irregularities in the microcircuit pattern itself, and may also identify specific types of particles and irregularities.
Various methods for ADC have been described in the patent literature. For example, U.S. Pat. No. 6,256,093 describes on-the-fly ADC in a scanned wafer. A light source illuminates the scanned wafer so as to generate an illuminating spot on the wafer. Light scattered from the spot is sensed by at least two spaced-apart detectors, and is analyzed so as to detect defects in the wafer and classify the defects into distinct defect types.
As another example, U.S. Pat. No. 6,922,482 describes automatically classifying a defect on the surface of a semiconductor wafer into one of a number of core classes, using a core classifier employing boundary and topographical information. The defect is then further classified into a subclass using a specific adaptive classifier that is associated with the core class and trained to classify defects from only a limited number of related core classes. Defects that cannot be classified by the core classifier or the specific adaptive classifiers are classified by a full classifier.